Instructions to use ornith-ai/Ornith-1.5-397B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ornith-ai/Ornith-1.5-397B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ornith-ai/Ornith-1.5-397B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ornith-ai/Ornith-1.5-397B")
model = AutoModelForMultimodalLM.from_pretrained("ornith-ai/Ornith-1.5-397B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ornith-ai/Ornith-1.5-397B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ornith-ai/Ornith-1.5-397B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ornith-ai/Ornith-1.5-397B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ornith-ai/Ornith-1.5-397B

SGLang

How to use ornith-ai/Ornith-1.5-397B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ornith-ai/Ornith-1.5-397B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ornith-ai/Ornith-1.5-397B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ornith-ai/Ornith-1.5-397B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ornith-ai/Ornith-1.5-397B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ornith-ai/Ornith-1.5-397B with Docker Model Runner:
```
docker model run hf.co/ornith-ai/Ornith-1.5-397B
```

SaylorTwift HF Staff commited on 1 day ago

Commit

9da5af2

verified ·

1 Parent(s): 8bc9685

Add evaluation results

Browse files

Files changed (1) hide show

.eval_results/ornith-1.5-397b.yaml +74 -0

.eval_results/ornith-1.5-397b.yaml ADDED Viewed

	@@ -0,0 +1,74 @@

+- dataset:
+    id: SWE-bench/SWE-bench_Verified
+    task_id: swe_bench_%_resolved
+  value: 86.0
+  date: "2026-08-18"
+  source:
+    url: https://huggingface.co/ornith-ai/Ornith-1.5-397B
+    name: "Ornith-1.5-397B model card"
+- dataset:
+    id: ScaleAI/SWE-bench_Pro
+    task_id: SWE_Bench_Pro
+  value: 65.1
+  date: "2026-08-18"
+  source:
+    url: https://huggingface.co/ornith-ai/Ornith-1.5-397B
+    name: "Ornith-1.5-397B model card"
+- dataset:
+    id: SWE-bench/SWE-bench_Multilingual
+    task_id: swe_bench_multilingual_%_resolved
+  value: 79.6
+  date: "2026-08-18"
+  source:
+    url: https://huggingface.co/ornith-ai/Ornith-1.5-397B
+    name: "Ornith-1.5-397B model card"
+- dataset:
+    id: datacurve/deep-swe
+    task_id: deep_swe
+  value: 56.0
+  date: "2026-08-18"
+  source:
+    url: https://huggingface.co/ornith-ai/Ornith-1.5-397B
+    name: "Ornith-1.5-397B model card"
+- dataset:
+    id: cais/hle
+    task_id: hle
+  value: 44.6
+  date: "2026-08-18"
+  source:
+    url: https://huggingface.co/ornith-ai/Ornith-1.5-397B
+    name: "Ornith-1.5-397B model card"
+  notes: "No tools."
+- dataset:
+    id: cais/hle
+    task_id: hle
+  value: 56.1
+  date: "2026-08-18"
+  source:
+    url: https://huggingface.co/ornith-ai/Ornith-1.5-397B
+    name: "Ornith-1.5-397B model card"
+  notes: "With tools."
+- dataset:
+    id: Idavidrein/gpqa
+    task_id: diamond
+  value: 92.8
+  date: "2026-08-18"
+  source:
+    url: https://huggingface.co/ornith-ai/Ornith-1.5-397B
+    name: "Ornith-1.5-397B model card"
+- dataset:
+    id: claw-eval/Claw-Eval
+    task_id: general
+  value: 81.4
+  date: "2026-08-18"
+  source:
+    url: https://huggingface.co/ornith-ai/Ornith-1.5-397B
+    name: "Ornith-1.5-397B model card"
+  notes: "Model card reports one aggregate score without a split breakdown; mapped to the general split."